1. Field of Invention
This invention relates in general to systems, and more particularly to monitoring multiple time series as a measurement of system behavior.
2. Description of Background
Large volumes of time series data are routinely generated in scientific, engineering, financial, and medical domains. A wide spectrum of applications monitor time series data for process and quality control, pattern discovery, and abnormality forecasting. Much study has focused on revealing the internal structure (e.g., autocorrelation, trends and seasonal variation) of time series, and recently, mining time series for knowledge discovery has received a lot of attention from data mining, information retrieval, and bioinformatics communities.
Examples of monitoring a large number of times series streams includes data collected by distributed sensors, real time quotes of thousands of securities, system events generated by a large number of networked hosts, and DNA expression levels gathered by the microarray technology for thousands of genes, etc.
One of the common tasks in monitoring multiple time series simultaneously is to find correlations among them. Discovering correlations is important to many applications for at least two reasons. First, fluctuation of values in one time series often depends on many factors. Separate analyses on single series are not sufficient to understand the underlying mechanism that produces the multiple interrelated time series. Second, monitoring tens of thousands of time series is a resource intensive task. Knowing the interrelationship among the time series may enable us to concentrate limited resources on as few time series as possible, as the behavior of other time series can be derived by these time series.
Thus, there is a need for a method that measures the relevance of multiple time series by leveraging state transition points and mutual information maximization.